Working Papers

[4] "The Subjective Risk and Return Expectations of Institutional Investors" (with Spencer Couts and Andrei Goncalves)

We use the long-term Capital Market Assumptions of major asset managers and investment consultants from 1987 to 2022 to study their subjective risk and return expectations across 19 asset classes. We find a strong and positive subjective risk-return tradeoff, with most of the level and variation in subjective expected returns arising from risk premia (beta compensation) rather than alphas. Subjective expected returns predict future realized returns both across asset classes and over time, with subjective risk premia driving most of this predictability. Subjective risk also predicts future realized risk, with stronger predictability across asset classes than over time.

[3] "Subjective Beliefs and the Portfolio Allocations of Institutional Investors" (with Aleksandar Andonov, Andrei Goncalves,   Spencer Couts and Andrea Rossi)

We study how subjective beliefs shape the portfolio allocations of institutional investors. Linking the multi-asset allocations of U.S. public pension funds to the long-term capital market assumptions of their consultants, we examine the extent to which differences in subjective expected returns, volatilities, and correlations map into differences in portfolio weights. We embed these belief inputs in a mean-variance framework that incorporates fund-consultant belief wedges, heterogeneous risk aversion, non-negative weight constraints, and a benchmarking incentive due to frictions. We find that pension fund allocations are significantly linked to belief-implied mean-variance efficient allocations across pension funds, across asset classes, and over time. Accounting for frictions is essential: it dramatically increases the pass through and explanatory power of beliefs to portfolio allocations. Overall, our results show that beliefs play a central role in institutional portfolio decisions and that frictions critically shape their transmission into observed allocations.

[2] "Institutional Investors' Subjective Risk Premia: Time Variation and Disagreement" (with Spencer Couts, Andrei Goncalves, and Yicheng Liu)

We study how institutional investors’ subjective risk premia shape variation in their expected returns over time and across institutions. Our analysis uses long-term Capital Market Assumptions from asset managers and investment consultants from 1987 to 2022. Most of the countercyclicality and overall time variation in institutional expected returns reflects variation in perceived market risk premia, not perceived mispricing or alphas more generally. This risk premium effect is driven almost entirely by variation in perceived risk quantities rather risk price (risk aversion). Expected return disagreement across institutions rises with macro-financial uncertainty and is also explained primarily by disagreement about market risk premia. However, unlike the time-series results, alphas account for a quantitatively important share of disagreement, and the price and quantity of risk contribute roughly equally to the risk premium effect. These findings provide benchmark moments that asset pricing models should match to be consistent with institutional investors’ beliefs.

[1] "Real Time Investor Alphas and the Survival of the Anomaly Zoo"  (with Andrei Goncalves and Richard Ogden)

Anomaly strategies generate positive and significant CAPM alphas even after becoming public information. Common explanations emphasize non-market risks, trading costs, and investment frictions. This paper introduces a complementary channel: allocation uncertainty. Investors who learn about an anomaly remain uncertain about the optimal weight for combining it with the market portfolio, making its future factor regression alpha unattainable. We introduce the real time investor alpha, which measures the Sharpe ratio improvement from adding an anomaly to the market portfolio using weights estimated in real time. Empirically, anomalies retain positive factor regression alphas after publication, but their average real time investor alpha is close to zero. Investors can profitably combine multiple anomalies, but only with shrinkage. We show theoretically that this uncertainty-induced shrinkage makes investors trade less aggressively than full information investors, allowing CAPM alphas to survive in equilibrium. This model explains 15% to 30% of the cross-sectional variation in anomaly alphas.